lebsral

Review DSPy code for correctness and best practices. Use when you want a code review of your DSPy program, need to check if your AI code follows best practices, want to find anti-patterns in your DSPy usage, or need a quality audit of your AI implementation. Also use for DSPy code review, is my DSPy code correct, review my AI code, best practices check, DSPy anti-patterns, code quality audit, am I

Build a conversational AI assistant with memory and state. Use when you need a customer support chatbot, helpdesk bot, onboarding assistant, sales qualification bot, FAQ assistant, or any multi-turn conversational AI. Also used for chatbot remember previous messages, conversational AI keeps forgetting context, build a helpdesk bot that actually works, chatbot drops context after a few turns, Inter

Chain multiple AI steps into one reliable pipeline. Use when your AI task is too complex for one prompt, you need to break AI logic into stages, combine classification then generation, do multi-step reasoning, build a compound AI system, orchestrate multiple models, or wire AI components together. Also used for LangChain LCEL alternative, how to chain LLM calls together, one prompt is not enough,

Verify and validate AI output before it reaches users. Use when you need guardrails, output validation, safety checks, content filtering, fact-checking AI responses, catching hallucinations, preventing bad outputs, or quality gates. Also used for - AI output looks right but is wrong, how to validate JSON from LLM, LLM returns invalid data, catch bad AI outputs before users see them, output quality

Pick the right DSPy module and architecture for your AI feature. Use when you are not sure whether to use Predict, ChainOfThought, ReAct, or a pipeline, need to choose between DSPy patterns, want architecture advice for your AI feature, or are deciding between a single module and a multi-step pipeline. Also use for which DSPy module should I use, Predict vs ChainOfThought, when to use ReAct, singl

Normalize and fix messy data fields using AI. Use when normalizing addresses, standardizing company names, fixing inconsistent date formats, cleaning CSV data before import, correcting typos in bulk data, normalizing phone number formats, standardizing job titles, cleaning up free-text fields, data quality improvement with AI, fixing formatting inconsistencies, bulk data normalization, preparing m

Build multiple AI agents that work together. Use when you need a supervisor agent that delegates to specialists, agent handoff, parallel research agents, support escalation (L1 to L2), content pipeline (writer + editor + fact-checker), or any multi-agent system. Also used for CrewAI alternative, AutoGen alternative, LangGraph multi-agent, agents that talk to each other, specialist agents with a su

Reduce your AI API bill. Use when AI costs are too high, API calls are too expensive, you want to use cheaper models, optimize token usage, reduce LLM spending, route easy questions to cheap models, or make your AI feature more cost-effective. Also used for GPT-4 costs too much for production, AI bill keeps growing, how to reduce OpenAI costs, optimize LLM token usage, smart model routing saves mo

Break a failing complex AI task into reliable subtasks. Use when your AI works on simple inputs but fails on complex ones, extraction misses items in long documents, accuracy degrades as input grows, AI conflates multiple things at once, results are inconsistent across input types, you need to chunk long text for processing, or you want to split one unreliable AI step into multiple reliable ones.

Detect fraud, unusual behavior, and anomalies in events or transactions using AI. Use when detecting fraud, flagging suspicious transactions, anomaly detection in logs, spotting unusual user behavior, abuse detection, identifying outliers in data, suspicious activity monitoring, fraud scoring, unusual pattern detection, flagging account takeover attempts, detecting bot traffic, abnormal usage patt

Describe your AI problem and get routed to the right skill with a ready-to-use prompt. Use when you are not sure which ai- skill to use, want help picking the right approach, or just want to describe what you need in plain language. Also use this when someone says I want to build an AI that..., how do I make my AI..., or describes any AI/LLM task without naming a specific skill, I need AI but do n

Fine-tune models on your data to maximize quality and cut costs. Use when prompt optimization hit a ceiling, you need domain specialization, you want cheaper models to match expensive ones, you heard fine-tuning will make us AI-native, you have 500+ training examples, or you need to train on proprietary data. Also use when you have spent weeks of manual iteration with no systematic improvement pat

Fix broken AI features. Use when your AI is throwing errors, producing wrong outputs, crashing, returning garbage, not responding, or behaving unexpectedly. Also use when you get Could not parse LLM output errors, DSPy program crashes, LLM timeout or rate limit errors, API key not working with DSPy, JSON parse error from LLM, model returns empty response, AI works sometimes but fails other times,

Make your AI follow rules and policies. Use when your AI breaks format rules, violates content policies, ignores business constraints, outputs invalid JSON, exceeds length limits, includes forbidden content, or does not comply with your specifications. Also use when LLM JSON output is unreliable, you get inconsistent formatting with random spaces and line breaks, or there is extraneous text and co

Generate synthetic training data when you do not have enough real examples. Use when you are starting from scratch with no data, need a proof of concept fast, have too few examples for optimization, cannot use real customer data for privacy or compliance, need to fill gaps in edge cases, have unbalanced categories, added new categories, or changed your schema. Also used for create training data wi

Generate smart notification messages from structured events using AI. Use when writing push notification copy, creating alert messages from events, building weekly digest emails, summarizing incidents from logs, personalizing notification text, generating Slack alerts from system events, smart email subject lines, aggregating multiple events into one digest message, notification copy for mobile ap

Measure and improve how well your AI works. Use when AI gives wrong answers, accuracy is bad, responses are unreliable, you need to test AI quality, evaluate your AI, write metrics, benchmark performance, optimize prompts, improve results, or systematically make your AI better. Also used for spent hours tweaking prompts, trial and error prompt engineering is not working, quality plateaued early, s

Scaffold a new AI feature powered by DSPy. Use when adding AI to your app, starting a new AI project, building an AI-powered feature, setting up a DSPy program from scratch, or bootstrapping an LLM-powered backend. Also used for DSPy quickstart, DSPy hello world, first DSPy program, getting started with DSPy, new to AI development, add AI to existing Python app, AI feature from zero to working, sc

Make your AI give the same answer every time. Use when AI gives different answers to the same question, outputs are unpredictable, responses vary between runs, you need deterministic AI behavior, or your AI is unreliable. Also used for same input gives different output every time, prompt sensitivity causes output changes with minor wording tweaks, reordering examples shifts accuracy dramatically,

Find and merge duplicate records across datasets using AI. Use when deduplicating contacts, merging customer records, entity resolution, matching records across systems, finding duplicate tickets, fuzzy matching company names, CRM deduplication, record linkage across databases, matching people across data sources, identifying the same entity in different formats, contact merge, account deduplicati

Auto-moderate what users post on your platform. Use when you need content moderation, flag harmful comments, detect spam, filter hate speech, catch NSFW content, block harassment, moderate user-generated content, review community posts, filter marketplace listings, or route bad content to human reviewers. Also used for build content moderation system, UGC moderation at scale, user-generated conten

Know when your AI breaks in production. Use when you need to monitor AI quality, track accuracy over time, detect model degradation, set up alerts for AI failures, log predictions, measure production quality, catch when a model provider changes behavior, build an AI monitoring dashboard, or prove your AI is still working for compliance. Also use when you are seeing silent quality drops in producti

Pull structured data from messy text using AI. Use when parsing invoices, extracting fields from emails, scraping entities from articles, converting unstructured text to JSON, extracting contact info, parsing resumes, reading forms, pulling data from transcripts (VTT, LiveKit, Recall), extracting fields from Langfuse traces, or any task where messy text goes in and clean structured data comes out.

Plan a multi-phase AI feature before building it. Use when you have a PRD or project idea and need to figure out the execution order, which skills to use in what sequence, or how to break an ambitious AI project into phases. Also use when you want to scope an AI feature, create a phased rollout plan, or figure out dependencies between AI components., help me figure out how to execute this, plan my

Build AI that answers questions about your database. Use when you need text-to-SQL, natural language database queries, a data assistant for non-technical users, AI-powered analytics, plain English database search, or a chatbot that talks to your database. Covers DSPy pipelines for schema understanding, SQL generation, validation, and result interpretation., text-to-SQL that actually works, AI SQL

Make AI solve hard problems that need planning and multi-step thinking. Use when your AI fails on complex questions, needs to break down problems, requires multi-step logic, needs to plan before acting, gives wrong answers on math or analysis tasks, or when a simple prompt is not enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery re

Build AI that recommends products, articles, or content based on user preferences. Use when building product recommendations, you-might-also-like features, personalizing feeds, content discovery, ranking by user preference, suggesting related items, building a recommendation engine, collaborative filtering with LLMs, re-ranking search results by relevance, curating personalized playlists, next-bes

Strip PII and sensitive data from text before processing with AI. Use when redacting personal information, GDPR compliance, anonymizing customer data, masking credit cards, redacting PHI for HIPAA, stripping emails and phone numbers, de-identifying medical records, removing names from transcripts, PII detection and replacement, building a data anonymization pipeline, sanitizing text before sending

Request or contribute a new AI skill that does not exist yet. Use when DSPy supports something but there is no skill for it — helps you build the skill and submit a PR, or file an issue requesting it. Also use when the user says there should be a skill for this, can we make a skill, I want to contribute a skill, none of the ai- skills cover my use case, how do I add a new skill, submit a skill, or

Rewrite text to match a different tone, reading level, or audience using AI. Use when rewriting content in a different tone, simplifying legal language, adapting text for a different audience, converting technical docs to plain English, making formal text casual, adjusting reading level, matching brand voice in existing content, paraphrasing for clarity, converting jargon-heavy text to simple lang

Score, grade, or evaluate things using AI against a rubric. Use when grading essays, scoring code reviews, rating candidate responses, auditing support quality, evaluating compliance, building a quality rubric, running QA checks against criteria, assessing performance, rating content quality, or any task where you need numeric scores with justifications. Also use when building an LLM as a judge, a

Build AI that searches your documents and answers questions. Use when building a knowledge base, help center Q&A, chatting with documents, answering questions from a database, search-and-answer over internal docs, customer support bot, or FAQ system. Also use when embedding search loses critical context, retrieval returns irrelevant results, the right document is buried deep in search results, RAG

Put your AI behind an API. Use when you need to serve AI features as web endpoints, add AI to an existing backend, deploy AI for other services to call, wrap a DSPy program in REST or HTTP, build an AI microservice, or put a language model behind FastAPI or Flask. Also use for deploy AI model to production, AI REST API, serve DSPy program over HTTP, Docker AI service, AI endpoint for mobile app, h

Auto-sort, categorize, or label content using AI. Use when sorting tickets into categories, auto-tagging content, labeling emails, detecting sentiment, routing messages to the right team, triaging support requests, building a spam filter, intent detection, topic classification, or any task where text goes in and a category comes out. Also use when classification accuracy varies between runs or sem

Stop your AI from making things up. Use when your AI hallucinates, fabricates facts, is not grounded in real data, does not cite sources, makes unsupported claims, or you need to verify AI responses against source material. Also use when your LLM makes up facts, responses are disconnected from the input, or outputs are not grounded in source documents. Covers citation enforcement, faithfulness ver

Condense long content into short summaries using AI. Use when summarizing meeting notes, condensing articles, creating executive briefs, extracting action items, generating TL;DRs, creating digests from long threads, summarizing customer conversations, or turning lengthy documents into bullet points. Also used for AI summary too generic, summarize Slack threads, condense customer feedback, meeting

Switch AI providers or models without breaking things. Use when you want to switch from OpenAI to Anthropic, try a cheaper model, stop depending on one vendor, compare models side-by-side, a model update broke your outputs, you need vendor diversification, or you want to migrate to a local model. Also use when your prompt broke after a model update, prompts that work for GPT-4 do not work for Clau

Build AI that takes actions, calls APIs, and does things autonomously. Use when you need AI to call APIs, use tools, perform calculations, search the web and act on results, interact with databases, or do multi-step tasks. Also AI that does things not just talks, tool-using AI agent, AI calls external APIs, function calling with DSPy, build AI that books appointments, AI workflow automation, agent

Find every way users can break your AI before they do. Use when you need to red-team your AI, test for jailbreaks, find prompt injection vulnerabilities, run adversarial testing, do a safety audit before launch, prove your AI is safe for compliance, stress-test guardrails, or verify your AI holds up against adversarial users. Covers automated attack generation, iterative red-teaming with DSPy, and

See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup., debug slow

Track which optimization experiment was best. Use when you have run multiple optimization passes, need to compare experiments, want to reproduce past results, need to pick the best prompt configuration, track experiment costs, manage optimization artifacts, decide which optimized program to deploy, or justify your choice to stakeholders. Covers experiment logging, comparison, and promotion to prod

Translate text between languages with AI while preserving brand voice and terminology. Use when translating app copy to Spanish, localizing marketing content, multilingual support tickets, i18n with AI, machine translation with brand voice, translating product descriptions, localizing help docs, batch translating i18n JSON files, glossary-enforced translation, AI-powered localization pipeline, tra

Analyze images and extract structured data from visual content using AI. Use when analyzing product photos, extracting text from screenshots, generating alt text for accessibility, visual question answering, categorizing images by content, reading receipts and invoices from photos, OCR with AI, describing images for search indexing, product photo categorization, document image processing, chart an

Generate articles, reports, blog posts, or marketing copy with AI. Use when writing blog posts, creating product descriptions, generating newsletters, drafting reports, producing marketing copy, creating documentation, writing email campaigns, or any task where AI writes long-form content from a topic or brief. Powered by DSPy content generation pipelines., AI blog writer, generate marketing copy

Use when you need to customize how DSPy formats prompts for a specific provider — switching from chat to completion format, forcing JSON output, or debugging prompt rendering issues. Common scenarios - debugging why your prompt looks wrong when sent to the model, switching from OpenAI to Anthropic and the formatting breaks, forcing the model to return valid JSON instead of markdown, working with c

REMOVED IN DSPy 3.x -- use dspy.Refine or dspy.BestOfN instead (see /dspy-refine, /dspy-best-of-n). Legacy documentation for dspy.Assert and dspy.Suggest kept for existing codebases only. For new code, use dspy.Refine (iterative improvement with feedback) or dspy.BestOfN (sampling, pick best). Also used for dspy.Assert, dspy.Suggest, runtime validation for LLM output, retry on bad output, backtrac

Use when you need to run DSPy modules asynchronously — FastAPI endpoints, concurrent LM calls, non-blocking execution, or integrating DSPy into async web frameworks. Common scenarios - serving DSPy behind FastAPI or Starlette, running multiple LM calls concurrently with asyncio.gather, non-blocking batch processing, combining async with streaming, or building async agent loops. Related - ai-servin

Use when output quality varies across runs and you want to sample multiple completions and pick the best — trading latency for reliability on high-stakes outputs. Common scenarios - generating multiple candidate answers and picking the highest-scoring one, improving reliability on high-stakes classification, reducing variance in creative generation, getting better summaries by sampling several and